As architects, we all know the importance of context. The right architecture for one context – say, an organically growing company – doesn’t work for a company growing by acquisition. The right technology strategy for a medium-size American company doesn’t work for a China-based one.

Well, the context for enterprise architecture itself is changing. We’ve got The Age Of The Customer forcing companies to transform outside-in. We have what is called technology consumerization – our business users have access to ever more powerful technology solutions independent of IT. We have digital-fueled business disruption, as described in James McQuivey’s book, Digital Disruption. And all this is driving the demand for greater business agility – the ability to quickly sense and adeptly respond to new opportunities and threats in their context.

What a great opportunity for enterprise architecture programs! But this is only possible if they shift from a focus on cost to a focus on opportunity, change from controlling to enabling technology, and adapt their practices to the need for quickness with “just enough insight.”

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The term “one-trick pony” allegedly originated back in the 19th-century days of the traveling circus, where low-end ones were sarcastically called “dog and pony shows.” The really bad ones got the reputation of having a pony that only knew one trick. Today many IT shops are in danger of becoming like those sad circuses, having one or at least a very limited set of technology tricks to help their firms seize opportunities quickly. For example, I routinely talk to business people who say they avoid IT at all costs when they have new analytic needs; at these firms, IT has only one response to all new requests for data – update the data warehouse or a data mart in a slow and expensive waterfall development process.

One term keeps occurring, as I talk to businesses about this issue — they want to be real-time. We’ve been using the term for years to talk about a wide range of things, from embedded C to extreme, low-latency analytics. I think all of these miss what the business is really after — the ability to use more information more quickly to take rapid action in response to unanticipated changes in their environment. Five-year technology strategies are out; but many can’t get their head around this new world, which is why a recent Forrester study showed that IT is increasingly losing control of technology spend. How do we get back in the game?

Companies like Barclays Wealth Management, Sears, and USAA are redefining their architecture with new tricks to be responsive in real-time by:

I recently had a conversation with a new EA practice leaders in the investment management business unit of a large multi-line insurance company. They wanted to hear my perspectives on what a world-class EA program should look like. They knew of all the traditional EA building blocks: standards and roadmaps, architecture domains, methodologies like TOGAF. They had a long list of things to do, but were uncertain about which to tackle first, and had a nagging feeling that these had little to do with world-class EA programs. We touched on EA maturity models, but quickly concluded that there isn’t an obvious and compelling business value proposition to simply ‘being mature’.

The conversation shifted to outcomes – what are the outcomes of a world-class EA program? IT cost reduction could be an outcome, and has been the raison d’etre of EA for years. IT solution design quality could be an outcome, and has been the justification for architects for longer than EA has been around. But these are all IT-centric outcomes.

We all know the world is changing. Digital capabilities are radically impacting our customers, the competitive landscape, the regulatory context, and the operating models of businesses. Kyle McNabb summarizes this very well in his blog post. The mantra today is business agility in the face of all these radical changes. Because of this, being IT-centric is no longer the hallmark of a world class EA program.

A recent survey of Enterprise Architects showed a lack of standards for data management.* Best practices has always been about the creation of standards for IT, which would lead us to think that lack of standards for data management is a gap.

Not so fast.

Standards can help control cost. Standards can help reduce complexity. But, in an age when a data management architecture needs to flex and meet the business need for agility, standards are a barrier. The emphasis on standards is what keeps IT in a mode of constant foundation building, playing the role of deli counter, and focused on cost management.

In contrast, when companies throw off the straight jacket of data management standards the are no longer challenged by the foundation. These organizations are challenged by ceilings. Top performing organizations, those that have had annual growth above 15%, are working to keep the dam open and letting more data in and managing more variety. They are pushing the envelope on the technology that is available.

Think about this. Overall, organizations have made similar data management technology purchases. What has separated top performers from the rest of organizations is by not being constrained. Top performers maximize and master the technology they invest in. They are now better positioned to do more, expand their architecture, and ultimately grow data value. For big data, they have or are getting ready to step out of the sandbox. Other organizations have not seen enough value to invest more. They are in the sand trap.

Standards can help structure decisions and strategy, but they should never be barriers to innovation.

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A recent survey of Enterprise Architects showed a lack of standards for data management.* Best practices has always been about the creation of standards for IT, which would lead us to think that lack of standards for data management is a gap.

Not so fast.

Standards can help control cost. Standards can help reduce complexity. But, in an age when a data management architecture needs to flex and meet the business need for agility, standards are a barrier. The emphasis on standards is what keeps IT in a mode of constant foundation building, playing the role of deli counter, and focused on cost management.

In contrast, when companies throw off the straight jacket of data management standards the are no longer challenged by the foundation. These organizations are challenged by ceilings. Top performing organizations, those that have had annual growth above 15%, are working to keep the dam open and letting more data in and managing more variety. They are pushing the envelope on the technology that is available.

Think about this. Overall, organizations have made similar data management technology purchases. What has separated top performers from the rest of organizations is by not being constrained. Top performers maximize and master the technology they invest in. They are now better positioned to do more, expand their architecture, and ultimately grow data value. For big data, they have or are getting ready to step out of the sandbox. Other organizations have not seen enough value to invest more. They are in the sand trap.

Standards can help structure decisions and strategy, but they should never be barriers to innovation.

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I recently had a client ask about MDM measurement for their customer master. In many cases, the discussions I have about measurement is how to show that MDM has "solved world hunger" for the organization. In fact, a lot of the research and content out there focused on just that. Great to create a business case for investment. Not so good in helping with the daily management of master data and data governance. This client question is more practical, touching upon:

what about the data do you measure?

how do you calculate?

how frequently do you report and show trends?

how do you link the calculation to something the business understands?

It seems to be popular these days amongst industry pundits to recommend that organizations add a new Cxx role: the Chief Data Officer (CDO). The arguments in favor of this move are exactly what you'd think: the rapidly accelerating importance of information in the enterprise, and, as important, the heightened perception of the importance of information by business executives. The attention on information comes from all the rich new data that simply didn't exist before: sensor data from the Internet Of Things, social media, process data -- really just the enormous volume of data resulting from the digitization of everything. Add to all that: new technology to handle big data in a reasonable time frame, user-friendly mobile computing in the form of tablets, data virtualization software and data warehouse appliances that significantly accelerate the process of getting at the information for analysis, and the promise of predictive analytics, and there's plenty of cause for an information management rennaisance out there. With a little luck, the activity it catalyzes will also improve enterprises' ability to manage the data and content that's not so new but also very important that we've been struggling with for the last decade or so.

The only argument against creating this role that I've run across is that if CIOs and CTOs did their jobs right, we wouldn't need this new role. That's pretty feeble since we're not just talking about IT's history of relative ineffectiveness in managing information outside of application silos (and don't get me started about content management) -- we're adding to that a significant increase in the value of information and a significant increase in the amount of available information. And then there's the fact that the data could be in the cloud and not managed by IT, and there's also a changing picture regarding risk that suggests a new approach.

I just came back from a Product Information Management (PIM) event this week had had a lot of discussions about how to evaluate vendors and their solutions. I also get a lot of inquiries on vendor selection and while a lot of the questions center around the functionality itself, how to evaluate is also a key point of discussion. What peaked my interest on this subject is that IT and the Business have very different objectives in selecting a solution for MDM, PIM, and data quality. In fact, it can often get contentious when IT and the Business don't agree on the best solution.

General steps to purchase a solution seem pretty consistent: create a short list based on the Forrester Wave and research, conduct an RFI, narrow down to 2-3 vendors for an RFP, make a decision. But, the devil seems to be in the details.

Is a proof of concept required?

How do you make a decision when vendors solutions appear the same? Are they really the same?

How do you put pricing into context? Is lowest really better?

What is required to know before engaging with vendors to identify fit and differentiation?

When does meeting business objectives win out over fit in IT skills and platform consistency?

The pace of technology-fueled business innovation is accelerating, and enterprise architects can take a leading role by helping their firms identify opportunities for shrewd investment. In our 2012 global state of EA online survey, we asked again what the most disruptive technologies would be; here’s what we found:

The results shouldn’t surprise anybody; however, if you are only looking at these, you are likely to get smacked in the face when you blink -- things are changing that fast. In the near future, new platforms built on today’s hot technologies will create more disruption. For example, by 2016 there will be 760 million tablets in use and almost one-third will be sold to business. Forrester currently has a rich body of research on mobility and other hot technologies, such as Forrester’s mobile eBusiness playbook and the CIO’s mobile engagement playbook. But by 2018, mobile will be the norm, so then what?

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There are interesting debates all around the globe about whether there is the need for a next gen EA framework. James Lapalme recently published an excellent article: Three Schools of Thought on Enterprise Architecture explaining the reasons of such debates.

In this article James identifies three schools of thoughts for EA, each with their own scope and purpose:

"Enterprise IT architecting" which addresses enterprise-wide IT, and the alignment of IT with business.

"Enterprise integrating" which addresses the coherency of the enterprise as a system with IT is only one component of the enterprise.

"Enterprise Ecological Adaptation" which addresses the enterprise in its larger environment